ALIF EXPLORATION
Selected Variables
base: Code of the patient
covariates:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Previous surgery - LEV
- LGap
- RLL
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
outcomes_ql:
- 2Y. ODI - Score (%)
- 2Y. SRS22 - SRS Subtotal score
- 2Y. SF36 - MCS
- 2Y. SF36 - PCS
outcomes_radiology:
- 6W. Major curve Cobb angle
- 1Y. Major curve Cobb angle
- 2Y. Major curve Cobb angle
- 6W. T1 Sagittal Tilt
- 1Y. T1 Sagittal Tilt
- 2Y. T1 Sagittal Tilt
- 6W. Sagittal Balance
- 1Y. Sagittal Balance
- 2Y. Sagittal Balance
- 6W. Global Tilt
- 1Y. Global Tilt
- 2Y. Global Tilt
- 6W. Lordosis (top of L1-S1)
- 1Y. Lordosis (top of L1-S1)
- 2Y. Lordosis (top of L1-S1)
- 6W. LGap
- 1Y. LGap
- 2Y. LGap
- 6W. Pelvic Tilt
- 1Y. Pelvic Tilt
- 2Y. Pelvic Tilt
predictive:
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Osteotomy
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Tobacco use_First Visit
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
demographic:
- Age
- Gender
- Prior Spine Surgery
- ASA classification
- 3CO
- BMI_First Visit
- Global Tilt
- ideal LL
- Lordosis (top of L1-S1)
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
expanded:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Previous surgery - LEV
- LGap
- RLL
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
- SRS22 - SRS Subtotal score_First Visit
- T1 Sagittal Tilt
- Sagittal Balance
- Global Tilt
- Lordosis (top of L1-S1)
- Pelvic TiltPropensity Scores Common Support
Model Stats
- Treatment proportion: 0.127
- Model Type: elastic_net
- Accuracy: 0.8965962
- Params: alpha: 0.2153846 lambda: 0.0142449
Average Treatment Effects - Radiology
Outcome: 6W. Major curve Cobb angle
Distribution:
0% 25% 50% 75% 100%
-65.000 -21.000 -10.440 -3.415 16.880
Model Type Y: boosting
RMSE: 16.3885663582152
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75
Model Type No: boosting
RMSE: 12.9342185886316
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75
ATE (Yes-No): -6.369 (Std.Error: 4.34)
Trimmed ATE (Yes-No): -6.261 (Std.Error: 4.522)
Upper ATE (Yes-No): -8.988 (Std.Error: 5.562)
Observational differences in treatment 1.867 (Yes-No)
treatment outcome
1: Yes 22.89611
2: No 21.02903`geom_smooth()` using method = 'loess' and formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. Major curve Cobb angle
Distribution:
0% 25% 50% 75% 100%
-64.00 -22.97 -9.93 -2.28 22.44
Model Type Y: boosting
RMSE: 18.7014985353854
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.875
Model Type No: boosting
RMSE: 14.5390640238122
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): 0.763 (Std.Error: 4.145)
Trimmed ATE (Yes-No): 1.048 (Std.Error: 4.304)
Upper ATE (Yes-No): -5.818 (Std.Error: 5.48)
Observational differences in treatment 3.198 (Yes-No)
treatment outcome
1: Yes 23.67687
2: No 20.47880`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 2Y. Major curve Cobb angle
Distribution:
0% 25% 50% 75% 100%
-60.00 -23.52 -9.53 -1.08 21.99
Model Type Y: boosting
RMSE: 19.6485039202452
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5
Model Type No: boosting
RMSE: 14.4612270116829
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -3.663 (Std.Error: 5.162)
Trimmed ATE (Yes-No): -3.507 (Std.Error: 5.352)
Upper ATE (Yes-No): -6.725 (Std.Error: 6.301)
Observational differences in treatment 1.849 (Yes-No)
treatment outcome
1: Yes 23.97219
2: No 22.12335`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. T1 Sagittal Tilt
Distribution:
0% 25% 50% 75% 100%
-23.631420 -5.244884 -1.457698 2.000000 18.000000
Model Type Y: boosting
RMSE: 6.43997981471071
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75
Model Type No: boosting
RMSE: 5.65914096441526
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.875
ATE (Yes-No): -6.694 (Std.Error: 1.3)
Trimmed ATE (Yes-No): -6.82 (Std.Error: 1.316)
Upper ATE (Yes-No): -3.487 (Std.Error: 4.366)
Observational differences in treatment -0.932 (Yes-No)
treatment outcome
1: Yes -3.688515
2: No -2.756435`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. T1 Sagittal Tilt
Distribution:
0% 25% 50% 75% 100%
-30.098675 -5.534988 -2.000000 1.480410 20.000000
Model Type Y: boosting
RMSE: 7.49531258000082
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5
Model Type No: boosting
RMSE: 5.87961904908195
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -3.655 (Std.Error: 2.081)
Trimmed ATE (Yes-No): -3.589 (Std.Error: 2.188)
Upper ATE (Yes-No): -4.898 (Std.Error: 3.385)
Observational differences in treatment 0.143 (Yes-No)
treatment outcome
1: Yes -2.502714
2: No -2.645953`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 2Y. T1 Sagittal Tilt
Distribution:
0% 25% 50% 75% 100%
-31.332362 -5.685001 -1.366539 1.078189 10.268933
Model Type Y: boosting
RMSE: 7.62852556132965
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.875
Model Type No: boosting
RMSE: 5.48318578880692
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5
ATE (Yes-No): -6.008 (Std.Error: 1.546)
Trimmed ATE (Yes-No): -6.012 (Std.Error: 1.62)
Upper ATE (Yes-No): -5.946 (Std.Error: 3.184)
Observational differences in treatment -0.997 (Yes-No)
treatment outcome
1: Yes -3.676968
2: No -2.680294`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. Sagittal Balance
Distribution:
0% 25% 50% 75% 100%
-194.79 -68.55 -27.01 2.10 114.15
Model Type Y: boosting
RMSE: 49.8876262654283
Params: nrounds: 100.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5
Model Type No: boosting
RMSE: 52.3973226579747
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -53.287 (Std.Error: 15.043)
Trimmed ATE (Yes-No): -54.461 (Std.Error: 15.861)
Upper ATE (Yes-No): -29.708 (Std.Error: 30.128)
Observational differences in treatment -8.982 (Yes-No)
treatment outcome
1: Yes 23.00886
2: No 31.99110`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. Sagittal Balance
Distribution:
0% 25% 50% 75% 100%
-237.47 -62.47 -28.13 7.24 109.54
Model Type Y: boosting
RMSE: 58.0223445603253
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 51.106528828063
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -45.357 (Std.Error: 14.192)
Trimmed ATE (Yes-No): -44.1 (Std.Error: 14.658)
Upper ATE (Yes-No): -69.328 (Std.Error: 21.342)
Observational differences in treatment -3.004 (Yes-No)
treatment outcome
1: Yes 34.28567
2: No 37.29009`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 2Y. Sagittal Balance
Distribution:
0% 25% 50% 75% 100%
-252.690 -56.225 -17.085 7.660 107.700
Model Type Y: boosting
RMSE: 73.5684668064938
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5
Model Type No: boosting
RMSE: 52.1538640450979
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -55.217 (Std.Error: 10.547)
Trimmed ATE (Yes-No): -54.356 (Std.Error: 10.892)
Upper ATE (Yes-No): -75.601 (Std.Error: 45.819)
Observational differences in treatment -14.719 (Yes-No)
treatment outcome
1: Yes 26.24857
2: No 40.96763`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. Global Tilt
Distribution:
0% 25% 50% 75% 100%
-68.6200 -18.0175 -6.0000 1.8425 16.0000
Model Type Y: boosting
RMSE: 15.8603884346037
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625
Model Type No: boosting
RMSE: 12.1952879947072
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -13.537 (Std.Error: 4.769)
Trimmed ATE (Yes-No): -13.486 (Std.Error: 4.91)
Upper ATE (Yes-No): -14.72 (Std.Error: 5.747)
Observational differences in treatment -5.812 (Yes-No)
treatment outcome
1: Yes 18.04944
2: No 23.86111`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. Global Tilt
Distribution:
0% 25% 50% 75% 100%
-62.630 -15.715 -5.100 1.000 26.000
Model Type Y: boosting
RMSE: 15.8815394939775
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625
Model Type No: boosting
RMSE: 11.3369466634336
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -15.6 (Std.Error: 2.981)
Trimmed ATE (Yes-No): -15.615 (Std.Error: 3.137)
Upper ATE (Yes-No): -15.28 (Std.Error: 7.357)
Observational differences in treatment -3.087 (Yes-No)
treatment outcome
1: Yes 22.36419
2: No 25.45107`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 2Y. Global Tilt
Distribution:
0% 25% 50% 75% 100%
-65.2300 -12.6725 -4.0450 2.1525 20.0000
Model Type Y: boosting
RMSE: 14.8396483659181
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 10.8552123298433
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -13.608 (Std.Error: 2.921)
Trimmed ATE (Yes-No): -13.469 (Std.Error: 2.916)
Upper ATE (Yes-No): -16.468 (Std.Error: 7.653)
Observational differences in treatment -2.237 (Yes-No)
treatment outcome
1: Yes 24.46276
2: No 26.70024`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. Lordosis (top of L1-S1)
Distribution:
0% 25% 50% 75% 100%
-65.22 -24.00 -9.38 0.00 29.00
Model Type Y: boosting
RMSE: 18.481531218616
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625
Model Type No: boosting
RMSE: 15.0145925501619
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -8.925 (Std.Error: 5.59)
Trimmed ATE (Yes-No): -8.902 (Std.Error: 5.87)
Upper ATE (Yes-No): -9.493 (Std.Error: 5.346)
Observational differences in treatment -3.333 (Yes-No)
treatment outcome
1: Yes -52.60333
2: No -49.27082`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. Lordosis (top of L1-S1)
Distribution:
0% 25% 50% 75% 100%
-67.87 -24.56 -7.22 0.00 23.38
Model Type Y: boosting
RMSE: 20.1781697031483
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 15.0823332236862
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -12.391 (Std.Error: 7.489)
Trimmed ATE (Yes-No): -12.404 (Std.Error: 7.842)
Upper ATE (Yes-No): -12.081 (Std.Error: 5.642)
Observational differences in treatment 0.311 (Yes-No)
treatment outcome
1: Yes -48.80097
2: No -49.11244`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 2Y. Lordosis (top of L1-S1)
Distribution:
0% 25% 50% 75% 100%
-65.5600 -22.5050 -8.1800 -0.2375 26.5200
Model Type Y: boosting
RMSE: 20.5465751210006
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75
Model Type No: boosting
RMSE: 14.943125692656
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -10.19 (Std.Error: 5.058)
Trimmed ATE (Yes-No): -10.198 (Std.Error: 5.399)
Upper ATE (Yes-No): -10.034 (Std.Error: 7.886)
Observational differences in treatment -2.919 (Yes-No)
treatment outcome
1: Yes -51.83774
2: No -48.91858`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. LGap
Distribution:
0% 25% 50% 75% 100%
-64.6206 -24.0000 -9.1254 0.2107 27.3800
Model Type Y: boosting
RMSE: 19.6693894753783
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625
Model Type No: boosting
RMSE: 15.13438406087
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -7.041 (Std.Error: 4.652)
Trimmed ATE (Yes-No): -6.936 (Std.Error: 4.849)
Upper ATE (Yes-No): -9.578 (Std.Error: 4.911)
Observational differences in treatment -4.195 (Yes-No)
treatment outcome
1: Yes 9.285117
2: No 13.480212`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. LGap
Distribution:
0% 25% 50% 75% 100%
-67.72420 -24.42425 -6.89620 0.58000 22.08000
Model Type Y: boosting
RMSE: 21.7975429638111
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75
Model Type No: boosting
RMSE: 15.2884074938048
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -10.667 (Std.Error: 6.576)
Trimmed ATE (Yes-No): -10.581 (Std.Error: 6.811)
Upper ATE (Yes-No): -12.641 (Std.Error: 7.021)
Observational differences in treatment -1.258 (Yes-No)
treatment outcome
1: Yes 12.69223
2: No 13.94980`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 2Y. LGap
Distribution:
0% 25% 50% 75% 100%
-65.65180 -22.33365 -8.57860 -0.58385 25.09440
Model Type Y: boosting
RMSE: 18.9052708924185
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 14.4735715266741
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.875
ATE (Yes-No): -11.022 (Std.Error: 4.653)
Trimmed ATE (Yes-No): -11.053 (Std.Error: 4.855)
Upper ATE (Yes-No): -10.421 (Std.Error: 6.815)
Observational differences in treatment -3.781 (Yes-No)
treatment outcome
1: Yes 10.30224
2: No 14.08358`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. Pelvic Tilt
Distribution:
0% 25% 50% 75% 100%
-36.4100 -8.2575 -2.0000 2.0000 14.4200
Model Type Y: boosting
RMSE: 10.705519947725
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 7.6937479159047
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -6.503 (Std.Error: 3.684)
Trimmed ATE (Yes-No): -6.473 (Std.Error: 3.851)
Upper ATE (Yes-No): -7.275 (Std.Error: 5.053)
Observational differences in treatment -4.452 (Yes-No)
treatment outcome
1: Yes 17.37857
2: No 21.83019`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. Pelvic Tilt
Distribution:
0% 25% 50% 75% 100%
-26.6200 -6.8975 -2.0150 1.3925 23.0000
Model Type Y: boosting
RMSE: 9.57611025832631
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5
Model Type No: boosting
RMSE: 6.76718706313625
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625
ATE (Yes-No): -9.522 (Std.Error: 2.1)
Trimmed ATE (Yes-No): -9.818 (Std.Error: 2.199)
Upper ATE (Yes-No): -2.757 (Std.Error: 3.84)
Observational differences in treatment -2.932 (Yes-No)
treatment outcome
1: Yes 19.57871
2: No 22.51027`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 2Y. Pelvic Tilt
Distribution:
0% 25% 50% 75% 100%
-25.630 -6.000 -1.440 2.595 12.500
Model Type Y: boosting
RMSE: 10.114111250265
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5
Model Type No: boosting
RMSE: 6.49481066983608
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -6.4 (Std.Error: 2.662)
Trimmed ATE (Yes-No): -6.596 (Std.Error: 2.801)
Upper ATE (Yes-No): -2.555 (Std.Error: 3.528)
Observational differences in treatment -1.502 (Yes-No)
treatment outcome
1: Yes 21.54000
2: No 23.04153`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'